/*
    * Licensed to the Apache Software Foundation (ASF) under one or more
    * contributor license agreements.  See the NOTICE file distributed with
    * this work for additional information regarding copyright ownership.
    * The ASF licenses this file to You under the Apache License, Version 2.0
    * (the "License"); you may not use this file except in compliance with
    * the License.  You may obtain a copy of the License at
    *
    *      http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   */
  package org.apache.commons.math4.legacy.genetics;
  
  
  import java.util.LinkedList;
  import java.util.List;
  
  import org.junit.Assert;
  import org.junit.Test;
  
  /**
   * This is also an example of usage.
   */
  public class GeneticAlgorithmTestBinary {
  
      // parameters for the GA
      private static final int DIMENSION = 50;
      private static final int POPULATION_SIZE = 50;
      private static final int NUM_GENERATIONS = 50;
      private static final double ELITISM_RATE = 0.2;
      private static final double CROSSOVER_RATE = 1;
      private static final double MUTATION_RATE = 0.1;
      private static final int TOURNAMENT_ARITY = 2;
  
      @Test
      public void test() {
          // to test a stochastic algorithm is hard, so this will rather be an usage example
  
          // initialize a new genetic algorithm
          GeneticAlgorithm ga = new GeneticAlgorithm(
                  new OnePointCrossover<>(),
                  CROSSOVER_RATE, // all selected chromosomes will be recombined (=crossover)
                  new BinaryMutation(),
                  MUTATION_RATE,
                  new TournamentSelection(TOURNAMENT_ARITY)
          );
  
          Assert.assertEquals(0, ga.getGenerationsEvolved());
  
          // initial population
          Population initial = randomPopulation();
          // stopping conditions
          StoppingCondition stopCond = new FixedGenerationCount(NUM_GENERATIONS);
  
          // best initial chromosome
          Chromosome bestInitial = initial.getFittestChromosome();
  
          // run the algorithm
          Population finalPopulation = ga.evolve(initial, stopCond);
  
          // best chromosome from the final population
          Chromosome bestFinal = finalPopulation.getFittestChromosome();
  
          // the only thing we can test is whether the final solution is not worse than the initial one
          // however, for some implementations of GA, this need not be true :)
  
          Assert.assertTrue(bestFinal.compareTo(bestInitial) > 0);
          Assert.assertEquals(NUM_GENERATIONS, ga.getGenerationsEvolved());
      }
  
  
  
  
      /**
       * Initializes a random population.
       */
      private static ElitisticListPopulation randomPopulation() {
          List<Chromosome> popList = new LinkedList<>();
  
          for (int i=0; i<POPULATION_SIZE; i++) {
              BinaryChromosome randChrom = new FindOnes(BinaryChromosome.randomBinaryRepresentation(DIMENSION));
              popList.add(randChrom);
          }
          return new ElitisticListPopulation(popList, popList.size(), ELITISM_RATE);
      }
  
      /**
       * Chromosomes represented by a binary chromosome.
       *
       * The goal is to set all bits (genes) to 1.
       */
      private static final class FindOnes extends BinaryChromosome {
  
          FindOnes(List<Integer> representation) {
             super(representation);
         }
 
         /**
          * Returns number of elements != 0
          */
         @Override
         public double fitness() {
             int num = 0;
             for (int val : this.getRepresentation()) {
                 if (val != 0) {
                     num++;
                 }
             }
             // number of elements >= 0
             return num;
         }
 
         @Override
         public AbstractListChromosome<Integer> newFixedLengthChromosome(List<Integer> chromosomeRepresentation) {
             return new FindOnes(chromosomeRepresentation);
         }
     }
 }